U.S. patent application number 14/347924 was filed with the patent office on 2014-09-04 for control of wind turbines.
This patent application is currently assigned to VESTAS WIND SYSTEMS A/S. The applicant listed for this patent is VESTAS WIND SYSTEMS A/S. Invention is credited to Robert Bowyer, Chris Spruce, Judith Turner.
Application Number | 20140248123 14/347924 |
Document ID | / |
Family ID | 47994304 |
Filed Date | 2014-09-04 |
United States Patent
Application |
20140248123 |
Kind Code |
A1 |
Turner; Judith ; et
al. |
September 4, 2014 |
CONTROL OF WIND TURBINES
Abstract
A wind turbine power plant comprises a plurality of wind
turbines, each having a rated output and under the control of a
power plant controller. The power plant also has a rated output
which may be over-rated in response to one or more of electricity
pricing data, power plant age and operator demand. The power plant
controller can send over-rating demand signals to individual
turbines. The controllers at the turbines include a fatigue life
usage estimator which estimates a measure of the fatigue life
consumed by key components of the turbine. If this measure exceeds
a target value for any component, over-rating is prevented at that
turbine.
Inventors: |
Turner; Judith; (Dorking,
GB) ; Spruce; Chris; (Leatherhead, GB) ;
Bowyer; Robert; (London, GB) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
VESTAS WIND SYSTEMS A/S |
Aarhus N |
|
DK |
|
|
Assignee: |
VESTAS WIND SYSTEMS A/S
Aarhus N
DK
|
Family ID: |
47994304 |
Appl. No.: |
14/347924 |
Filed: |
September 28, 2012 |
PCT Filed: |
September 28, 2012 |
PCT NO: |
PCT/DK2012/050363 |
371 Date: |
March 27, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61541121 |
Sep 30, 2011 |
|
|
|
Current U.S.
Class: |
415/1 ;
415/15 |
Current CPC
Class: |
F05B 2270/103 20130101;
Y02E 10/72 20130101; F05B 2270/332 20130101; Y02E 10/723 20130101;
F03D 7/043 20130101; F05B 2270/20 20130101; F05B 2270/109 20130101;
F03D 7/0292 20130101; F03D 7/042 20130101 |
Class at
Publication: |
415/1 ;
415/15 |
International
Class: |
F03D 7/04 20060101
F03D007/04 |
Foreign Application Data
Date |
Code |
Application Number |
Sep 30, 2011 |
DK |
PA 2011 70539 |
Claims
1. A controller for a wind turbine, comprising a turbine optimiser
and a lifetime usage estimator, the turbine optimiser outputting
setpoints for operating parameters of the wind turbine based on a
power demand input and an input from the lifetime usage estimator,
wherein the lifetime usage estimator calculates a measure of the
fatigue life consumed by each of a plurality of turbine components
based on a lifetime usage algorithm for each component, the
lifetime usage algorithms operating on values of variables
affecting the fatigue lifetime of the components, the values being
obtained from or derived from sensors on the wind turbine.
2. A controller according to claim 1, wherein the setpoints output
by the turbine optimiser are at least one of power, torque and
speed setpoints.
3. A controller according to claim 1, wherein the wind turbine is a
constant speed turbine and the setpoints output by the optimiser
are active power setpoints.
4. A controller according to claim 1, wherein the input from the
lifetime usage estimator comprises a measurement of fatigue life
consumed by each component.
5. A controller according to claim 1, wherein the input from the
lifetime usage estimator comprises a measurement of rate of usage
of fatigue life by each component.
6. A controller according to any of claim 1, wherein the controller
is an over-rating controller.
7. A controller according to any of claim 1, wherein the turbine
optimiser compares the proportion of the fatigue life consumed by
the components with a target consumption based on the age of the
component and prevents over-rating of the turbine if the fatigue
life consumed by any component is greater than the target
consumption for that component.
8. A controller according to any of claim 1, wherein the turbine
optimiser compares the proportion of the fatigue life consumed by
the most damaged components with a target consumption based on the
age of that component and prevents over-rating of the turbine if
the fatigue life consumed is greater than the target consumption
for that component.
9. A controller according to claim 7, wherein the over-rating
comprises speed and torque over-rating and wherein the fatigue life
of each component is speed sensitive, torque sensitive or speed and
torque sensitive, wherein over-rating of the speed is prohibited if
the consumption of fatigue life by a speed sensitive component
exceeds the target consumption, over-rating of torque is prohibited
if the consumption of fatigue life by a torque sensitive component
exceeds the target consumption and over-rating of speed and torque
is prohibited if the consumption of fatigue life by a torque and
speed component exceeds the ideal consumption.
10. A controller according to claim 1, wherein the turbine
optimiser communicates with a wind power plant controller and
receives the power demand input from the wind power plant
controller.
11. A controller according to claim 1, wherein the controller is
located at the wind turbine for which it generates setpoint
signals.
12. A controller according to claim 1, comprising a store storing a
library of lifetime usage algorithms, wherein the lifetime usage of
each of the components is calculated using at least one of the
algorithms.
13. A controller according to claim 1, wherein the turbine
components comprise one or more of blade components, blade bearing
components, blade pitch system components, main shaft, main shaft
bearing, gearbox, generator, converter, electrical power systems
transformer, nacelle bedplate, yaw system, tower or foundation.
14. A controller according to claim 13, wherein the blade
components comprise blade structure and blade bolts.
15. A controller according to claim 1, wherein the lifetime usage
algorithm for a component estimates a stress cycle range and mean
value based on received input values.
16. A controller according to claim 15, wherein the stress cycle
and mean values are inputs to a stress cycle damage algorithm which
provides as its output, the measure on consumption of fatigue
life.
17. A controller according to claim 1, wherein the turbine
optimiser receives an input of operational constraints indicating
restrictions on operating parameters of the turbine.
18. A controller according to claim 1, wherein the turbine
optimiser comprises a set point selector which receives the power
demand input and the measure of lifetime consumed for the
components and periodically calculates optimal operating set points
for the turbine parameters, and a constraint unit which receives
the optimal set points, the measure of lifetime usage and the power
demand and prevents sending over-rated setpoints if the measure of
fatigue life consumed by a component exceeds a target value, the
constraint unit outputting operating set points to the turbine at a
frequency greater than the frequency of receipt of optimal set
points.
19. A controller according to claim 1, wherein the setpoints output
by the turbine optimiser are further based on operational
constraints input to the turbine optimiser.
20. A wind turbine comprising a controller according to claim
1.
21. A method of controlling a wind turbine, comprising obtaining
values of variables affecting the fatigue lifetime of one or more
components of the turbine from turbine sensors, applying a fatigue
lifetime usage algorithm to the variables to determine a measure of
the fatigue life consumed by each of a plurality of turbine
components, inputting the measures of fatigue life consumption and
a power demand to a turbine optimiser, and generating setpoints for
operating parameters of the wind turbine by the turbine
optimiser.
22. A method according to claim 21, wherein the setpoints output by
the turbine optimiser are at least one of power, torque and speed
setpoints.
23. A method according to claim 21, wherein the wind turbine is a
constant speed turbine and the setpoints output by the optimiser
are active power setpoints.
24. A method according to claim 21, wherein the measure of fatigue
life comprises an estimate of fatigue life consumed by each
component.
25. A method according to claim 21, wherein the measure of fatigue
life comprises a measurement of rate of usage of fatigue life by
each component.
26. A method according to claim 21, wherein the controller is an
over-rating controller.
27. A method according to claim 21, wherein the turbine optimiser
compares the proportion of the fatigue life consumed by the
components with a target consumption based on the age of the
component and prevents over-rating of the turbine if the fatigue
life consumed by any component is greater than the target
consumption for that component.
28. A method according to claim 21, wherein the turbine optimiser
compares the proportion of the fatigue life consumed by the most
damaged component with a target consumption based on the age of
that component and prevents over-rating of the turbine if the
fatigue life consumed is greater than the target consumption for
that component.
29. A method according to claim 27, wherein the over-rating
comprises speed and torque over-rating and wherein the fatigue life
of each component is speed sensitive, torque sensitive or speed and
torque sensitive, wherein over-rating of the speed is prohibited if
the consumption of fatigue life by a speed sensitive component
exceeds a target consumption, over-rating of torque is prohibited
if the consumption of fatigue life by a torque sensitive component
exceeds the ideal consumption, and over-rating of speed and torque
is prohibited if the consumption of fatigue life by a torque and
speed component exceeds the target consumption.
30. A method according to claim 21, wherein the turbine optimiser
communicates with a wind power plant controller and receives the
power demand input from the wind power plant controller.
31. A method according to claim 21, wherein the controller is
located at the wind turbine for which it generates setpoint
signals.
32. A method according to claim 21, wherein a library of lifetime
usage algorithms is stored at the turbine, wherein the lifetime
usage of each of the components is calculated using at least one of
the algorithms.
33. A method according to claim 21, wherein the turbine components
comprise one or more of blade components, blade bearing components,
blade pitch system components, main shaft, main shaft bearing,
gearbox, generator, converter, electrical power systems
transformer, nacelle bedplate, yaw system, tower or foundation.
34. A method according to claim 33, wherein the blade components
comprise blade structure and blade bolts.
35. A method according to claim 21, wherein the lifetime usage
algorithm for a component estimates a stress cycle range and mean
value based on received input values.
36. A method according to claim 35, wherein the stress cycle and
mean values are inputs to a stress cycle damage algorithm which
provides as its output, the measure on consumption of fatigue
life.
37. A method according to 21, wherein the turbine optimiser
receives an input of operational constraints indicating
restrictions on operating parameters of the turbine.
38. A method according to 21, wherein the setpoints generated by
the turbine optimiser are further based on operational constraints
input to the turbine optimiser.
39. A method according to claim 21, wherein the turbine optimiser
periodically calculates optimal operating set points for the
turbine parameters on the basis of received power demand inputs and
the measure of lifetime consumed for the components, and prevents
sending over-rated setpoints if the measure of fatigue life
consumed by a component exceeds a target value.
40. A method of over-rating a wind turbine comprising receiving an
over-rating demand signal from a power plant controller,
determining a measure of the fatigue life consumed by each of a
plurality of turbine components based on a lifetime usage algorithm
for each component and sensed parameter values for that component,
generating and sending to the turbine at least one of a power a
torque set point in accordance with the over-rating demand set
point, wherein the over-rating set points are not sent to the
turbine if the measure of the fatigue life consumed exceeds a
target value for a component.
41. A controller for a wind power plant, the power plant comprising
a plurality of wind turbine generators each having a plurality of
components, and comprising a plurality of further components
between the wind turbine generator and a grid connection, the
controller comprising a wind turbine lifetime usage estimator for
estimating a measure of the fatigue life consumed by the
components, and a wind power plant lifetime usage estimator for
estimating a measure of fatigue life consumed by the plurality of
further components.
Description
[0001] This invention relates to control of wind turbines and wind
power plants and, in particular to control methods and apparatus
which take into account the condition of the wind turbine when
making control decisions.
[0002] The rated power of a wind turbine is defined in IEC 61400 as
the maximum continuous electrical power output which a wind turbine
is designed to achieve under normal operating and external
conditions. Large commercial wind turbines are generally designed
for a lifetime of 20 years and their rated power output takes into
account that lifespan.
[0003] Wind turbines are commonly operated as part of a wind power
plant comprising a plurality of wind turbines. U.S. Pat. No.
6,724,097 discloses operation of such a wind plant. The output of
each turbine is determined and one or more turbines controlled so
that the output power of one or more turbines is reduced if the
total output exceeds the rated output of the plant. Such an
arrangement is useful as the sum of the individual rated powers may
exceed the rated output of the wind power plant, but at any one
time not all turbines may be operating at full capacity; some may
be shut down for maintenance and some may be experiencing less than
ideal wind conditions.
[0004] While the approach taken in U.S. Pat. No. 6,724,097 deals
with avoiding overproduction by a wind power plant, the total
output of the plant may not reach the rated plant power if some
turbines are shut down, for example for maintenance, or are not
operating at their rated power, for example because the local wind
conditions at those turbines do not allow rated power output to be
achieved. It is economically desirable, therefore, to boost the
output of one or more of the turbines to increase the total output
of the power plant to its rated output. However, such boosting
risks damaging the turbines.
[0005] U.S. Pat. No. 6,850,821 discloses a wind turbine controller
that has measured stress conditions as an input allowing it to
control the output power as a function of measured stress. Thus,
for example, power output may be reduced in very turbulent wind
conditions in comparison to less turbulent conditions having the
same average wind speed. US-A-2006/0273595 discloses intermittently
operating a wind power plant at an increased rated power output
based on an assessment of operating parameters with respect to
component design ratings and intermittently increasing the output
power of a wind turbine based on the assessment. The present
invention aims to provide improved methods and apparatus for
controlling wind turbines.
[0006] According to the invention there is provided a controller
for a wind turbine, comprising a turbine optimiser and a lifetime
usage estimator, the turbine optimiser outputting setpoints for
operating parameters of the wind turbine based on a power demand
input and an input from the lifetime usage estimator, wherein the
lifetime usage estimator calculates a measure of the fatigue life
consumed by each of a plurality of turbine components based on a
lifetime usage algorithm for each component, the lifetime usage
algorithms operating on values of variables affecting the fatigue
lifetime of the components, the values being obtained from or
derived from sensors on the wind turbine.
[0007] The invention also provides a method of controlling a wind
turbine, comprising obtaining values of variables affecting the
fatigue lifetime of one or more components of the turbine from
turbine sensors, applying a fatigue lifetime usage algorithm to the
variables to determine a measure of the fatigue life consumed by
each of a plurality of turbine components, inputting the measures
of fatigue life consumption and a power demand to a turbine
optimiser, and generating setpoints for operating parameters of the
wind turbine by the turbine optimiser.
[0008] The invention further provides a method of over-rating a
wind turbine comprising receiving an over-rating demand signal form
a power plant controller, determining a measure of the fatigue life
consumed by each of a plurality of turbine components based on a
lifetime usage algorithm for each component and sensed parameter
values for that component, generating and sending to the turbine at
least one of a power a torque set point in accordance with the
over-rating demand set point, wherein the over-rating set points
are not sent to the turbine if the measure of the fatigue life
consumed exceeds a target value for a component.
[0009] Embodiments of the invention have the advantage that
setpoint signals such as power and torque are conditional on the
estimated condition of turbine components. The fatigue life usage
of key components can be estimated from sensed parameters or
parameters derived from sensed parameters in conjunction with an
appropriate fatigue lifetime usage algorithm. Once the fatigue
lifetime usage has been calculated it can be compared with the
target lifetime usage of components, based for example on the time
since commissioning. If the estimated fatigue life usage of any
component is above the target, over-rating of the turbine may be
suppressed. This enables over-rating to be achieved without the
risk of damage to turbine components and without the risk of
shortening the life of components which might counteract the
financial benefits of over-rating.
[0010] In one embodiment of the invention, the setpoints output by
the turbine optimiser are torque and speed setpoints. The input
from the lifetime usage estimator may comprise one or both of a
measurement of fatigue life consumed by each component and a
measurement of rate of usage of fatigue life by each component. The
measurement of fatigue life usage enables a determination of the
overall condition of a component to be made whereas the measurement
of the rate of fatigue rate usage enables over-rating to be
prevented if the fatigue life is being consumed quickly, even if
the current fatigue life is less than the target at that time.
[0011] Measurement of the rate of change of life used may also
enable the identification of the influence of Low-Cycle Fatigue
(LCF), since the completion of each single cycle of large and
long-duration stress or strain will result in a sudden large change
in lifetime used, which can be detected as an event and used to
moderate over-rating in response thereto.
[0012] In one embodiment the lifetime usage calculations are
implemented on a turbine that has already been in service and
recorded historical data of the turbine's operation. The historical
data may then be used by means of appropriate calculations to set
the initial values for the lifetime usage estimators and to set the
initial strategy for the over-rating control. By setting the
initial values the need to operate the turbine with lifetime usage
calculations for an initial period, typically 1-year of operation,
before an over-rating control strategy can be implemented may be
avoided.
[0013] In one embodiment of the invention the turbine optimiser
compares the proportion of the fatigue life consumed by the
components with a target consumption based on the age of the
component and prevents over-rating of the turbine if the fatigue
life consumed by any component is greater than the target
consumption for that component. In another embodiment the turbine
optimiser compares the proportion of the fatigue life consumed by
the most damaged components with a target consumption based on the
age of that component and prevents over-rating of the turbine if
the fatigue life consumed is greater than the target consumption
for that component.
[0014] In one embodiment, the over-rating comprises speed and
torque over-rating and wherein the fatigue life of each component
is speed sensitive, torque sensitive or speed and torque sensitive,
wherein over-rating of the speed is prohibited if the consumption
of fatigue life by a speed sensitive component exceeds the target
consumption, over-rating of torque is prohibited if the consumption
of fatigue life by a torque sensitive component exceeds the target
consumption and over-rating of speed and torque is prohibited if
the consumption of fatigue life by a torque and speed component
exceeds the ideal consumption. Thus, over-rating is only prohibited
if a given component is above its target fatigue life and is
sensitive to the parameter that is being over-rated.
[0015] In one embodiment the turbine optimiser communicates with a
wind power plant controller and receives the power demand input
from the wind power plant controller. The controller may be located
at the wind turbine for which it generates setpoint signals. The
controller may comprise a store storing a library of lifetime usage
algorithms, wherein the lifetime usage of each of the components is
calculated using at least one of the algorithms. Such an
arrangement is advantageous as it enables new algorithms to be
added to the library to estimate fatigue life usage for additional
components of to provide a new algorithm for existing
components.
[0016] The turbine components the fatigue life of which is
estimated may be one or more of blade components, bearing
components, blade pitch control components, main shaft, gearbox,
generator, converter, transformer, yaw system, tower or foundation.
The blade components may comprise blade structure, blade bearings
and blade bolts.
[0017] In one embodiment, the lifetime usage algorithm for a
component estimates a stress cycle range and mean value based on
received input values. The stress cycle and mean values may be
inputs to a stress cycle damage algorithm which provides as its
output, the measure on consumption of fatigue life.
[0018] In one embodiment, the turbine optimiser receives an input
of operational constraints indicating restrictions on operating
parameters of the turbine. The turbine optimiser may comprise a set
point selector which receives the power demand input and the
measure of lifetime consumed for the components and periodically
calculates optimal operating set points for the turbine parameters,
and a constraint unit which receives the optimal set points, the
measure of lifetime usage and the power demand and prevents sending
over-rated setpoints if the measure of fatigue life consumed by a
component exceeds a target value, the constraint unit outputting
operating set points to the turbine at a frequency greater than the
frequency of receipt of optimal set points.
[0019] The invention also resides in a wind turbine having a
controller as defined above
[0020] The invention further provides a controller for a wind power
plant, the power plant comprising a plurality of wind turbine
generators each having a plurality of components, and comprising a
plurality of further components between the wind turbine generator
and a grid connection, the controller comprising a wind turbine
lifetime usage estimator for estimating a measure of the fatigue
life consumed by the components, and a wind power plant lifetime
usage estimator for estimating a measure of fatigue life consumed
by the plurality of further components.
[0021] Embodiments of the invention will now be described, by way
of example only, and with reference to the accompanying drawings,
in which:
[0022] FIG. 1 is a schematic view of a known wind power plant
control regime using a power plant controller;
[0023] FIG. 2 is a graph of wind speed against power showing a
power curve for a typical wind turbine;
[0024] FIG. 3 is a schematic view of a wind power plant control
regime embodying the present invention;
[0025] FIG. 4 is a similar view to FIG. 3 showing a refinement of
the control regime;
[0026] FIG. 5 is a similar view to FIG. 3 showing a further
refinement of the control regime;
[0027] FIG. 6 is a schematic view of a power plant set point
controller;
[0028] FIG. 7 is a graph of torque against speed showing operating
constraints for a wind turbine;
[0029] FIG. 8 is a graph illustrating the use of slope control in
over-rating; and
[0030] FIG. 9 is a graph illustrating the use of offset control in
over-rating;
[0031] FIGS. 10 a)-d) illustrate the relationship between fatigue
and over-rating;
[0032] FIG. 11 illustrates a turbine optimiser; and
[0033] FIG. 12 illustrates how over-rating is prevented when the
lifetime usage of a component exceeds its design limit at a given
component age
[0034] The following description addresses the general control of
turbines in a wind turbine power plant, the control of output power
from those turbines, and the optimisation of operating parameters
such as speed and torque within individual turbines based on set
points provided from the power plant controller. It describes
control regimes which are both devised by a multi-turbine
controller and sent as commands to individual turbines, and control
regimes which are implemented by individual turbines and then
communicated to a multi-turbine controller such as a power plant
controller.
[0035] FIG. 1 shows, schematically, a conventional wind power plant
10 comprising a plurality of wind turbines 20 each of which
communicates with a power plant controller PPC 30. The PPC 30 can
communicate bi-directionally with each turbine. The turbines output
power to a grid connection point 40 as illustrated by the thick
line 50.
[0036] In operation, and assuming that wind conditions permit, each
of the wind turbines 20 will output maximum active power up to
their nominal set point. This is their rated power as specified by
the manufacturer. The power that is output to the grid connection
point is simply the sum of the outputs of each of the turbines.
[0037] FIG. 2 illustrates a conventional power curve 55 of a wind
turbine plotting wind speed on the x axis against power output on
the y axis. Curve 55 is the normal power curve for the wind turbine
and defines the power output by the wind turbine generator as a
function of wind speed. As is well known in the art, the wind
turbine starts to generate power at a cut in wind speed Vmin. The
turbine then operates under part load (also known as partial load)
conditions until the rated wind speed is reached at point Vr. At
the rated wind speed at point Vr. the rated (or nominal) generator
power is reached and the turbine is operating under full load. The
cut in wind speed in a typical wind turbine is 3 m/s and the rated
wind speed is 12 m/s. Point Vmax is the cut out wind speed which is
the highest wind speed at which the wind turbine may be operated
while delivering power. At wind speeds equal to and above the cut
out wind speed the wind turbine is shut down for safety reasons, in
particular to reduce the loads acting on the wind turbine.
[0038] As described above, the rated power of a wind turbine is
defined in IEC 61400 as the maximum continuous electrical power
output which a wind turbine is designed to achieve under normal
operating and external conditions. Therefore, a conventional wind
turbine is designed to operate at the rated power so that the
design loads of components are not exceeded and that the fatigue
life of components is not exceeded.
[0039] As shown in FIG. 2, in embodiments of the invention the
turbine is controlled such that it can produce more power than the
rated power as indicated by shaded area 58. The term "over-rating"
is understood to mean producing more than the rated active power
during full load operation. When the turbine is over-rated, the
turbine is run more aggressive than normal and the generator has a
power output which is higher than the rated power for a given wind
speed.
[0040] The over-rating is characterised by a transient behaviour.
When a turbine is over-rated it may be for as short as a few
seconds, or for an extended period of time if the wind conditions
and the fatigue life of the components are favourable to
over-rating.
[0041] The over-rating power level may be up to 30% above the rated
power output.
[0042] The PPC controller 30 is shown schematically for ease of
illustration. It communicates with each of the turbines and can
receive data from the turbines, such as pitch angle, rotor speed,
power output etc. and can send commands to individual turbines,
such as set points for pitch angle, rotor speed, power output etc.
The PPC 30 also receives commands from the grid, for example from
the grid operator to boost or reduce active or reactive power
output in response to demand or a fault on the grid. Although not
shown in the schematic figure, each wind turbine also has its own
controller which is responsible for operation of the turbine and
communicates with the PPC 30.
[0043] The PPC controller 30 receives power output data from each
of the turbines and is therefore aware of the active and reactive
power output by each turbine and by the plant as a whole at the
grid connection point 40. If required, the PPC controller 30 can
receive an operating set point for the power plant as a whole and
divide this among each of the turbines so that the output does not
exceed the operator assigned set point. This power plant set point
may be anywhere from 0 up to the rated power output for the plant.
The "rated power" or "nominal power" output for the plant is the
sum of the rated power output of the individual turbines in the
plant. The power plant set point may even be above the rated power
output of the plant, i.e. the whole plant is over-rated. This is
discussed further below.
[0044] FIG. 3 shows a first embodiment of the invention. Instead of
receiving an input directly from the grid connection, the power
plant controller 30 receives a signal which is a measure of the
difference between the total power plant output and the nominal
power plant output. This difference is used to provide the basis
for over-rating by individual turbines. In this embodiment, which
is only one example, the actual output of the power park is
subtracted from the nominal or rated output of the power park at
subtractor 60. The difference, shown as error signal e in FIG. 3 is
input to an integrator 70. The integrator includes in-built
saturation which prevents integral wind up which is a well-known
problem in controllers where a large change in set point occurs and
the integral terms accumulate a significant error during the rise
(wind up), thus overshooting and continuing to increase as this
accumulated error is offset by errors in the other direction
(unwound).
[0045] The output from integrator 70 is input to an amplifier 80
which applies a fixed gain G which scales the integrator output to
provide an over-rating amount which is then provided to the
controller 30 and sent by the controller to each of the turbines
20. In theory, only a single turbine may be over-rated, but it is
preferred to over-rate a plurality of the turbines, and most
preferred to send the over-rating signal to all the turbines. The
over-rating signal sent to each turbine is not a fixed control but
an indication of a maximum amount of over-rating that each turbine
may perform. Each turbine has an optimiser, which may be located at
the turbine or centrally, and which is described in detail below,
which will determine whether the turbine can respond to the
over-rating signal and, if so, by what amount. For example, where
the optimiser determines that conditions at a given turbine are
favourable and above rated wind speed it may respond positively and
the given turbine is over-rated. As the optimisers implement the
over-rating signal, the output of the power plant will rise and so
the error signal produced by the subtractor 60 will decrease. The
integrator will reach equilibrium as the error either reaches zero
or the integrator saturates.
[0046] Thus, in this embodiment an over-rating signal is generated.
This signal is indicative of the amount of over-rating that may be
performed by turbines of the power plant as a whole. However, each
turbine responds individually to the over-rating signal in
accordance with its optimiser. If conditions are such that the
total optimisation results in over-rating that threatens to exceed
the power plant nominal output, the difference will reduce and
individual optimisers will reduce the amount of over-rating
applied.
[0047] FIG. 4 shows a modification of the arrangement of FIG. 3.
The FIG. 4 arrangement takes into account communications delays
which may occur in a real power plant between the PPC 30 and the
turbines 20. This is important as the over-rating signal is
communicated from PPC 30 to the turbines 20. If the value tmG is
too large, where t is delay time, m is the ratio of change in
over-rating request to power plant output change and G is the basic
feedback gain, the system will overshoot, oscillate or become
unstable. This value is a measure of the time taken for the
turbines to react to over-rating commands from the PPC 30. To
ensure that tmG is maintained within an acceptable range an upper
bound may be placed on t and m when calculating the maximum
feedback gain. However, this approach makes the controller slow to
respond to changes in power plant output. This is undesirable when
the output is too low and is unacceptable when the output is too
high as such operation could lead to component damage.
[0048] The arrangement of FIG. 4 overcomes this problem. The
individual turbines are interrogated via their respective
controllers by the PPC 30 to calculate the value of m. The
arrangement of FIG. 4 is similar to FIG. 3 except that the gain of
amplifier 85 is expressed as G/m and an input 100 from the turbines
to the amplifier is shown. The delay between the PPC 30 and the
turbines 20 is illustrated as delay 90. Thus the only parameter
that is determined from the upper bound is t. This approach enables
the controller to respond more quickly to changes in power plant
output.
[0049] In this example, as with the FIG. 3 example, the over-rating
command sent to each turbine is the same.
[0050] It will be appreciated that the basic approach of FIG. 3 may
be used where the delay between the controller 30 and the turbines
is negligible. In practice, the delay will be determined by a
number of factors but the proximity of the PPC 30 to the turbines
will play a large part in determining the delay. At present a PPC
can poll all turbines in a large power plant in about 20 seconds
but it is anticipated that this time will reduce to less than 1
second or even 10 s of milliseconds in the near future.
[0051] In the previous two examples, the same over-rating set point
signal is sent to each turbine using the total power plant output
to provide a control input. In the embodiment of FIG. 5, each
turbine is given its own over-rating amount. Thus in FIG. 5 a
central optimiser 110 provides an input into the PPC 30. The
central optimiser 110 receives an input 120 from each turbine which
indicates the over-rating capability of that turbine. That input
will depend on a variety of factors such as the local wind
conditions, the present cost of electricity generated and the age
or fatigue damage of the turbine and will be provided by the
individual turbine controller. The central optimiser 110 will
calculate an over-rating value for each turbine and communicate
that value to each turbine based on the present over-rating
capability of the turbine. Of course the PPC 30 will take other
factors into account, such as the need to ensure that the total
power output does not exceed the rated output for the power plant.
The optimiser will base its decisions on the effect of its actions
on the fatigue damage of the turbine components and, in FIG. 5,
this is performed centrally for all turbines.
[0052] Thus FIGS. 3 to 5 illustrate ways in which the over-rating
of each turbine may be implemented via a power plant controller
either by generating a common over-rating command for each turbine
or by generating an individual over-rating command for each
turbine.
[0053] The examples given above each enable the power plant output
set point to be tracked, which in turn makes it possible to vary
that power plant set point. FIG. 6 illustrates an additional,
optional level of control for controlling this power plant set
point. This controller introduces a power plant set-point
controller PPSC which produces set points based either on the value
of power that can be generated which will depend, for example, on
the time of day and year, or on some other external variable such
as the age of the turbine or the need for the turbine operator to
generate cash flow In this example, each turbine may control its
own fatigue life through an individual turbine optimiser or control
of fatigue may be through a central optimiser as in the example of
FIG. 5. In FIG. 6, PPST is a power plant set point tracker and
corresponds to the optimiser of FIG. 5.
[0054] In a first variant of the FIG. 6 embodiment, the power plant
output set point is manually moved, or scheduled to move depending
on the date. Over the course of a year, a number of set point
changes may be scheduled. The purpose of this is to benefit from
feed-in tariffs or power purchase agreements and aids the power
plant operator's net present value. In addition to seasonal
variations, day-night variations in set points may be scheduled to
take into account higher day time electricity prices. These are
examples only and more advanced variations in electricity prices
may be scheduled in a similar manner to help the power park
operator maximise their return from the turbines.
[0055] As well as daily fluctuations in electricity prices there
are slower prices observed due to wider market effects such as the
prices of raw materials such as oil and gas. Merely scheduling
changes in power plant operating set points does not take these
changes into account as they are not cyclical or necessarily
predictable. Instead, the real time price of electricity on the
spot market for the geographical area to be supplied by the power
plant can provide an additional or an alternative input to the
controller. Thus the power set point is higher when the oil or gas
price is above a threshold value and lower when the oil or gas
price falls below that threshold. The turbines are commanded to
over-rate if their local controllers will permit it when the set
point is higher so that the power plant operator can benefit from
the higher prices in the spot market. It is likely that this
approach will have no overall effect on fatigue lifetimes as the
median point for the set point is chosen such that the turbines
will spend as much time at the higher set point as at the lower set
point.
[0056] In addition or alternately to control based on spot market
prices, the controller may take into account the cost of
electricity being traded on the forwards markets which give a
strong indication of the likely price of electricity some hours,
days or even weeks in the future. These markets are partially
driven by load forecasting which takes into account, for example,
expected weather conditions and may be used as an input to the
controller to assist in reaching the optimal set-point to
control.
[0057] In the embodiments described, the output of turbines is
over-rated as the total output of the power plant is below the
nominal output of the plant. This could be for a variety of
reasons. For example, if the total rated output of all turbines is
equal to the rated output of the power plant, over-rating may be
used if some turbines are shut down for maintenance or are not
operating at rated power, for example because the local wind
conditions do not permit it.
[0058] Alternatively, the power plant may be designed to have a
rated power output that is higher than the sum of the rated outputs
of all the turbines. This is advantageous as over-rating may then
be used even when all turbines are at rated output. This enables
the plant operator easily to take advantage of changes in operating
tariffs as described above. The approach outlined above with
respect to FIG. 6 enables the power plant operator to benefit from
favourable market conditions and tariffs by using over-rating and
so boosting income generated from the power plant. The operator may
choose to use this embodiment of the invention to over-rate at any
time when additional revenue is required, even if the market data
or the tariffs are not particularly favourable at the time. The
embodiment gives the operator the ability to generate additional
cash-flow which may be required for a variety of business
reasons.
[0059] The embodiment described with respect to FIGS. 3 to 6 shows
how over-rating can be used to boost the output of individual
turbines in response to a detected shortfall in power plant output
or in response to external economic conditions. FIGS. 7-9 are
concerned with the actual optimisation of turbines for over-rating
operation, and show how the over-rating command may be
implemented.
[0060] FIG. 7 is a graph of generator torque against generator
rotational speed for a wind turbine. Curves P.sub.1 and P.sub.2 are
lines of constant power corresponding to power set-points P.sub.1,
P.sub.2. They are curved as power is the product of torque and
rotational speed. An over-rating command from the PPC 30 takes the
form of a shift in the power set-point to a new value. The turbine
must then select an operating speed and torque to deliver that
power.
[0061] A turbine has hard constraints defined as the maximum and
minimum torque and speed at which it can operate. These constraints
are imposed by the controller and dictated by factors such as noise
limits, gearbox lubrication, component lifetime etc. These
constraints are referred to as hard constraints as the controller
may not violate them except in the extreme case of performing a
shutdown. Although these constraints are rigid, they may vary over
time.
[0062] The controller may also impose soft constraints which are
intended to prevent the turbine shutting down during over-rating,
typically as thermal limits or maximum generator speed are
approached. A temperature increase in key components will occur
during over-rating, for example throughout the drive train, and
could trip shutdown. Soft constraints may be lower than hard
constraints but result in the controller reducing the amount of
over-rating rather than performing a shut down. Thus the turbine
optimiser may include soft constraint values for drive train
related parameters and generator speed. When the controller detects
that a measured value is approaching a soft constraint value the
over-rating signal is reduced.
[0063] Thus, referring to FIG. 7, on the graph of Torque against
Rotational Speed there is a box 200 within which the turbine can
operate. The box is bounded by maximum and minimum speed and
torque. The purpose of the turbine optimiser is to choose the
optimum operating point for the turbine. In FIG. 5 the optimiser is
shown as a central unit which performs calculations for a plurality
of turbines, possibly all turbines of the power plant. This need
not be the case and the optimiser can be performed on a computer
physically located at a turbine, for example, as part of the
existing turbine controller. In that case, the data is passed over
the communications link to the PPC 30. The term `turbine optimiser`
therefore refers to the selection of set points for a given turbine
rather than implying any location.
[0064] It can be seen from FIG. 7 that the turbine cannot achieve
operation at any point on constant power curve P.sub.1, which is,
at all times outside the box 200. In this case, if the PPC 30
requests a power set point P.sub.1 at a given turbine, the turbine
optimiser will select the optimal rotational speed and torque at
the top right hand corner 210 of the box. If the PPC 30 requests a
power set point P2 at a given turbine, the line of constant power
P.sub.2 passes through the box, and so any point on that part of
the line that passes through the box could be chosen as the
operating point. The purpose of the turbine optimiser is to choose
the best point along this part of the curve. Although the figure
shows generator rotational speed, the term rotational speed may
refer to the rotational speed of the generator, the rotor or the
speed anywhere along the drive train. Although the absolute values
are different, they are all related.
[0065] Although not shown in the figure, if a constant power curve
were to go completely below the box 200 there are two available
choices. Firstly, the turbine shuts down as any set-points within
the box would produce a power output above the power set point.
Secondly, the turbine sets the rotational speed and torque as the
bottom left corner of the box 200, by analogy to the case with
curve P.sub.1, and advises the power plant controller 30 that it is
running above the requested power set point. The PPC 30 can then
optimise the scenario by lowering the set-points for one or more
other turbines. However, if all turbines, or a substantial
percentage, were in the bottom left corner, at least some would
have to be shut down.
[0066] Any point inside box 200 on the power set-point line is
valid. The following section describes how the set-point (or line
of constant power) is chosen having regard to the fatigue lifetime
of the turbine and components of the turbine.
[0067] The description above with respect to FIGS. 3 to 5 explained
how a common over-rating signal or set point sent from the PPC 30
is used to control over-rating by all turbines to control the
overall power plant output. However, over-rating carries inherent
risks, particularly to the integrity of turbine components and it
is important to control the extent to which over-rating is used
over the lifetime of a turbine. One way in which this may be
achieved is for each turbine to respond to the common over-rating
signal or set point in a way that best suits itself. This
calculation or assessment may be made either at the individual
turbines as part of their central process, or at the PPC 30 which
may perform the calculation individually for multiple turbines
based as data received from those turbines.
[0068] Thus, when the over-rating demand is received at each
turbine from the PPC 30, each turbine processes and responds to
this signal taking fatigue into account. A turbine may not
over-rate or may not over-rate at the level requested if the effect
on the fatigue lifetime of critical components is too great.
Examples of critical components include the rotor blades, blade
pitch systems, main bearing, gearbox, generator, converter,
transformer, yaw system, tower and foundations. This will depend on
the conditions at the turbine as well as the lifetime history of
the turbine. For example, a turbine that is near the end of its
life expectancy may be highly fatigued and so not suited to run at
the over-rating level demanded. If the power plant output is
insufficient as some or all of the turbines are operating under the
demanded over-rating level for fatigue saving, the over-rating
demand will keep rising until it reaches its set-point or
saturates.
[0069] Where a feedback system is used, such as in FIGS. 3 and 4,
each turbine can vary its over-rating response according to
lifetime usage. The over-rating set point sent from PPC 30 is
processed through a response function, examples of which are
described below in FIGS. 8 and 9. In these figures the turbine
over-rating response is shown on the Y axis and the selected
response is then sent to the system that chooses rotational speed
and torque as described in the previous section. Thus, in the graph
of FIG. 8, a slope control approach is adapted. Here the controller
has issued a 5% over-rating demand to the turbines. Ideally, the
turbine will respond with a 5% over-rating. If the output
stabiliser, which forms part of the controller requires so, the
turbine may respond with the 5% over-rating, over-riding fatigue
issues. A highly fatigued turbine will de-rate when the request is
zero or to slightly over-rate as shown in dashed line 300 in FIG.
8. Low fatigued turbines may over-rate even when the over-rating
request from the controller is zero as shown by dashed line 302 in
FIG. 8. The slope of these lines may vary according to the degree
of fatigue that has been experienced by the turbine and will affect
the value of m, the ratio of change in over-rating request to power
plant output change described with reference to FIG. 4 above.
[0070] In FIG. 8, the dashed line 304 passing through the origin
represents a 1:1 ratio of response to demand that would be provided
by a turbine with an expected degree of fatigue.
[0071] FIG. 9 shows an alternative approach although it is stressed
that FIGS. 8 and 9 only show two of a large number of possible
approaches. In FIG. 9, the axes are the same as in FIG. 8 and
dashed line 304 also represents a 1:1 response from a turbine with
expected fatigue. However, in this case, as shown by dashed line
306, if the turbine is sufficiently highly fatigued it will never
over-rate as the function will drop below the X axis completely.
Similarly, if the fatigue is sufficiently low, the turbine will
always over-rate. There will be no rapid changes in the responses
as the plant demand changes as the slope is constant.
[0072] In the description of FIG. 7, the application of hard
constraints to the speed and torque set points was described. These
fatigue control soft constraints may be applied before the hard
constraints. Thus, the choice of set point within the box 200 of
FIG. 7 is affected by the fatigue or lifetime usage
information.
[0073] When assessing fatigue of different components of the wind
turbines, different components will fatigue at different rates
under various conditions. Some component's fatigue life will be
more sensitive to speed and others will be more sensitive to
torque. The turbine components can be split into speed sensitive
components and torque sensitive components and the slope and/or
position of the line for the two response functions of FIGS. 8 and
9 is then chosen according to the worst of each group.
[0074] In order to make operation at above rated power less
damaging and have less fatigue damage, the critical components when
considering speed and torque related fatigue damage may be
upgraded. For example, if it is established that the gearbox is the
critical fatigue related component, the gearbox may be upgraded
relative to the other components so that the overall fatigue
expectancy falls and it becomes more acceptable to over-rate the
turbine and the time for which the turbine can be over-rated
increases.
[0075] Thus, the embodiments of FIGS. 8 and 9 provide for fatigue
control within the context of an over-rating system that is based
on feedback.
[0076] FIG. 5 described an over-rating approach that was based on
direct calculation of over-rating amounts rather than feedback
based on a difference signal at the power plant output. Fatigue
control may be incorporated into this approach. The PPC 30 is
responsible for setting the set points for each turbine and also
chooses power and torque set points. By using a state-based system,
where the states are the accumulated fatigue for each turbine, and
the inputs are power or speed and torque set points, as similar
control based on fatigue may be achieved as the PPC 30 will be
aware of fatigue data communicated from individual turbines which
can then be taken into account when setting the power or speed and
torque set points.
[0077] Thus, embodiments of the invention provide a variety of
controllers which enable wind turbines of a power plant to be
over-rated. Over-rating may be by a common control provided in
response to a measured output that is below the power plant nominal
output or it may be by optimisation of individual turbines.
Over-rating may additionally, or alternatively, be based on outside
economic factors based on the current price of generated power and
expected or anticipate changes in that cost. Moreover, when
determining the extent to which turbines can be over-rated, the
fatigue life of turbine components can be taken into account so
enabling the lifetime of the turbine to be preserved and, where
appropriate, additional revenue to be generated through
over-rating.
[0078] The various embodiments described may be combined to provide
a system which enables over-rating both to boost output where the
power plant is at below nominal output and to take into account
external economic factors such as a controller may also incorporate
control based on fatigue lifetimes.
[0079] Thus, in the embodiments described, a power plant having a
plurality of wind turbines aims to supply the grid with an amount
of power agreed upon in advance. The power plant controller manages
how much power is extracted from each turbine in order to match the
demand. Conventionally, the power demand sent from the PPC to the
individual turbines is restricted by their respective nameplate
ratings. In the embodiments described, the turbines restrict their
own production and the power demand from the PPC is sent to a
Turbine Optimizer (TO) on each turbine. This Optimizer is designed
to compute and send speed and torque set-points to the Production
Controller. The set-point is chosen to maximise the power produced
by the turbine across its lifetime whilst keeping the loads within
their design limits. The design limits for a turbine are made up of
the fatigue and extreme load limits of all the components that make
up a turbine. Alternatively, other set-point signals could be sent
and in one embodiment of the invention at least one of power,
torque and speed set-points are sent.
[0080] To ensure the fatigue load limits of all components remain
within their design lifetimes, the loads it experiences (be they
bending moments, temperatures, forces or motions for example) may
be measured and the amount of component fatigue life consumed
calculated, for example using a well known technique such as a
rainflow count and Miner's rule or a chemical decay equation. The
individual turbines can then be operated in such a way as to not
exceed the design limits. A device for the measuring of the fatigue
life consumed for a given component is referred to as its Lifetime
Usage Estimator (LUE). The output from these LUES can be used in
two ways. The LUE can inform the turbine whether the total fatigue
experienced at a given point in time is below or above the level
the turbine is designed to withstand, and the TO can decide to
over-rate when the damage is below the expected level. The LUEs can
also be used to measure the rate of accumulation of fatigue, as
opposed to an absolute level. If the fatigue lives of the
components are being consumed quickly, it may be more prudent to
not over-rate the turbine even if its current fatigue life is less
than expected at that time. The rate of usage of fatigue life may
then be one input to the over-rating controller and assist in the
decision whether or not to over-rate.
[0081] In practice it is not appropriate to measure all the load
signals on all the components and instead LUEs are used for a
subset of all the components on the turbine. In order to prevent
the components whose lifetime used is not measured with an LUE from
reaching their fatigue limits, and also prevent components from
exceeding extreme limits, constraints are placed on the turbine
operation based upon values of measurable signals (for example
temperature or electrical current). These constraints are referred
to as operational constraints. Operational constraint controllers
(OCCs) define how the turbine's behaviour should be restricted in
order to prevent the measured signals from exceeding these
operational constraints or triggering alarms which may result in
turbine shutdown. For example in the case of an operational
constraint on the temperature of a bulbar, the operational
constraint controller may reduce the power reference sent to the
Production Controller by an amount inversely proportional to the
difference between the temperature limit and the current measured
temperature. Another use for operational constraint controllers
could be to restrict the turbine operation based upon the produced
noise. This controller would exploit a model of how an operating
point translates to a measure of noise.
[0082] In order to prevent the component extreme loads from
reaching their limits, constraints on the torque, speed and power
are defined. This may be achieved by running simulations offline
and judging the operating points that can be achieved without the
probability of exceeding an extreme load limit being greater than a
pre-determined amount. A more advanced way would be to select the
limits in terms of the current environmental conditions, for
example, if the current wind conditions were high turbulence, the
limits would be lower than if they were low turbulence. These
conditions could be judged using a Liar unit, data from a MET mast,
or turbine signals.
[0083] As described, the extent to which a given turbine
over-rates, may, in one aspect of the invention, vary according to
the price the operator is paid for electricity at any given time,
thus maximising the income from the investment. This aspect may be
incorporated with lifetime usage estimation transmitting a measure
of production importance with the power demand and using the
measure to relax or restrict limits on rate of accumulation of
fatigue or probability of extreme loads exceeding their design
values. Forecasts of the weather based upon meteorological data may
also be exploited to determine when over-rating could be most
valuable over the prediction horizon.
[0084] As is clear from the foregoing description, the decision
whether to over-rate may be made by the turbine itself or by a
centralised controller. When operating a set of turbines, it may be
more prudent to compare the conditions of the various turbines to
decide which ones should over-rate, by how much and in what way.
This may be achieved in two separate ways. In the first
implementation, the lifetime usage estimators and operational
constraint controllers still exist on the individual turbines and
these provide the centralized controller with constraints on
torque, speed and power for each turbine in the plant. The
centralized controller then performs an optimization to minimize
the deviation of total power produced from the grid demand, and
distribute the loads between the turbines in a manner matching
their current state and the environmental conditions they are
seeing (or expected to see). This optimization could also exploit
information about the turbine locations and current wind direction
in order to minimize the loads resulting from aerodynamic
interaction.
[0085] In the second implementation, turbines are allowed to
exchange information with a subset (or the whole set) of turbines
in the power plant. The information exchanged would not be the
components lifetimes used, rather a certificate relating to its
current condition and ability to produce in the future. This would
perform less well than the global optimal achieved in the first
implementation but would have significantly reduced communication
and computational demands. This system would mimic the way in which
internet routers manage their transfer rate based upon the
aggregate cost per communication link and the previous amount of
data sent (TCP-IP).
[0086] The lifetime usage estimators will now be described in more
detail. The algorithm required to estimate lifetime usage will vary
from component to component and the lifetime usage estimators
comprise a library of lifetime usage estimator algorithms including
some or all of the following: load duration, load revolution
distribution, rainflow counting, stress cycle damage, temperature
cycle damage, generator thermal reaction rate, transformer thermal
reaction rate and bearing wear. Additionally other algorithms may
be used. As mentioned above, lifetime usage estimation may only be
used for selected key components and the use of a library of
algorithms enables a new component to be selected for LUE and the
suitable algorithm selected from the library and specific
parameters set for that component part.
[0087] In one embodiment, lifetime usage estimators are implemented
for all major components of the turbine including the blade
structure, blade bearing components, blade pitch system components,
main shaft, main shaft bearing, gearbox, generator, converter,
electrical power systems transformer, nacelle bedplate, yaw system,
tower and the foundation. In any implementation it may be decided
to omit one or more of these components and/or to include further
components.
[0088] As examples of the appropriate algorithms, rainflow counting
may be used in the blade structure, blade bolts, pitch system, main
shaft system, converter, yaw system, tower and foundation
estimators. In the blade structure algorithm, the rainflow count is
applied to the blade root bending flap wise and edgewise moment to
identify the stress cycle range and mean values and the output is
sent to the stress cycle damage algorithm. For the blade bolts, the
rainflow count is applied to the bolt bending moment to identify
stress cycle range and mean values and the output sent to the
stress cycle damage algorithm. In the pitch system, main shaft
system, tower and foundation estimators the rainflow counting
algorithm is also applied to identify the stress cycle range and
mean values and the output sent to the stress cycle damage
algorithm. The parameters to which the rainflow algorithm is
applied may include:
Pitch system--pitch force; Main shaft system--main shaft torque;
Tower--tower stress; Foundation--foundation stress.
[0089] In the yaw system the rainflow algorithm is applied to the
tower top torsion to identity the load duration and this output is
sent to the stress cycle damage algorithm. In the converter,
generator power and RPM is used to infer the temperature and
rainflow counting is used on this temperature to identify the
temperature cycle and mean values. The output is then sent to the
converter damage algorithm.
[0090] Lifetime usage in the blade bearings may be monitored either
by inputting blade flap wise load and pitch velocity as inputs to
the load duration algorithm or to a bearing wear algorithm. For the
gearbox, the load revolution duration is applied to the main shaft
torque to calculate the lifetime used. For the generator, generator
RPM is used to infer generator temperature which is used as an
input to the thermal reaction rate generator algorithm. For the
transformer, the transformer temperature is inferred from the power
and ambient temperature to provide an input to the transformer
thermal reaction rate algorithm.
[0091] Where possible it is preferred to use existing sensors to
provide the inputs on which the algorithms operate. Thus, for
example, it is common for wind turbines to measure directly the
blade root bending edgewise and flap wise moment required for the
blade structure, blade bearing and blade bolts estimators. For the
pitch system, the pressure in a first chamber of the cylinder may
be measured and the pressure in a second chamber inferred, enabling
pitch force to be calculated. These are examples only and other
parameters required as inputs may be measured directly or inferred
from other available sensor outputs. For some parameters, it may be
advantageous to use additional sensors if a value cannot be
inferred with sufficient accuracy.
[0092] The algorithms used for the various types of fatigue
estimation are known and may be found in the following standards
and texts:
Load Revolution Distribution and Load Duration:
[0093] Guidelines for the Certification of Wind Turbines,
Germainischer Lloyd, Section 7.4.3.2 Fatigue Loads
Rainflow:
[0093] [0094] IEC 61400-1 `Wind turbines--Part 1: Design
requirements, Annex G
Miners Summation:
[0094] [0095] IEC 61400-1 `Wind turbines--Part 1: Design
requirements, Annex G Power Law (Chemical decay): [0096] IEC
60076-12 `Power Transformers--Part 12: Loading guide for dry-type
power transformers`, Section 5
[0097] The frequency with which lifetime usage is calculated may
vary. In one embodiment, the lifetime of a component that has been
used is calculated every few minutes and expressed in years. The
rate of lifetime usage may be calculated every minute. However,
other time intervals may be used. The calculated values are
provided to the turbine optimiser which therefore receives values
for all major components every few minutes and usage rate values
for all major components every minute.
[0098] The turbine optimizer is illustrated in FIG. 11. The turbine
optimizer operates the turbine at a power level that does not
exceed that sent by the PPC and outputs the optimal level of torque
and speed based on information from the lifetime usage estimator
and the OCC.
[0099] As can be seen from FIG. 11, the turbine optimiser 400
includes a set-point selector 410 and a fast constraint
satisfaction unit 420. The set-point selector receives as its
inputs the PPC demand, operational constraints from the OCC and the
lifetime usage data for the major components as described above. In
the FIG. 11 example the input is the absolute value of lifetime
usage rather than the rate of usage. The set-point selector outputs
optimal set-points to the fast constraint satisfactions unit
periodically, for example between every minute and every few
minutes. The fast constraint satisfaction unit 420 also receives as
inputs the PCC demand signal, the lifetime usage date and the
operating constraints and outputs speed and torque set points
periodically. In the example shown, set-points are output at the
frequency of demand signals received from the PPC.
[0100] Of the components for which lifetime usage is determined,
each will be classified as speed sensitive if the damage
accumulated correlates with speed over-rating percentage only and
torque sensitive if the damage accumulated correlates with the
torque over-rating percentage only. Components may be generic is
they are sensitive to both torque and speed.
[0101] As mentioned, the set point selector 410 chooses the optimal
speed and torque set-points. This is done on a slow time scale Ts
which is in the order of minutes. The Set-Point Selector update
rate Ts, is chosen to maximise performance whilst ensuring the
Over-rating controller does not interfere with existing controllers
in the turbine software.
[0102] The set-point selector 410 receives the lifetime usage
estimates for all estimated components and selects the value
corresponding to the most damaged component; that with the greatest
used life. If that component has consumed more of its fatigue life
than it has been designed to have used at that point in time (the
Set-Point Selector outputs Optimal speed and power Set-Points equal
to their respective rated values. Thus, in that circumstance there
is no over-rating. This is illustrated in FIG. 12 which shows the
design fatigue level as a straight line graph of time, measure from
installation to decommission dates, against lifetime usage
estimate. In the figure the accumulated fatigue at a time `today`
is greater than the design level and so no over-rating would be
permitted. FIG. 12 is schematic only and a straight line graph may
not reflect the desired accumulation of lifetime and the rate of
usage will depend on the season.
[0103] If any of the speed sensitive components have used more of
their fatigue lives than their design value at that point in time,
the Set-point selector outputs an Optimal speed Set-point equal to
rated speed and if any of the torque sensitive components have used
more of their fatigue lives than their design value at that point
in time, the Set-point selector outputs an Optimal torque Set-point
equal to rated torque. The Set-Point Selector chooses an Optimal
Set-Point to maximise the power produced subject to constraints
from the PPC and Operational Constraint Controllers sampled at the
beginning of the time-step. The Set-Point Selector also attempts to
equalize the damage to the most damaged speed and torque sensitive
components.
[0104] The Fast constraint satisfaction unit in this example
operates at a higher frequency than the set-point selector and
applies saturations to the Optimal speed and torque Set-Points,
limiting the outputs to the limits provided by the Operational
Constraint Controllers and PPC.
[0105] The Fast constraint satisfaction block does not allow the
Turbine Optimiser to send set-points over-rated by speed/torque if
any of the speed/torque sensitive components have consumed more
than their target life. Similarly, the Turbine Optimiser will not
send an over-rated power set-point if any of the generic components
have consumed more than their target life.
[0106] The embodiments described contemplate over-rating based on
torque and speed. Over-rating may also be used in constant speed
turbines, for example constant speed active stall turbines. In this
case, only the power signal is over-rated and each turbine in the
power plant, or each turbine in a subset of the power plant, sends
an over-rating demand to the PPC which monitors the total output
and reduces the amount of over-rating if the total output is above
the rated output of the power plant. Alternatively only the power
signal may be over-rated. In practice, this is likely to be rarely
necessary as, dependent on weather conditions, not all turbines
will be over-rating and some may not be generating any power, for
example as they are shut down for maintenance. Alternatively, a
power regulation model uses a control loop which compares wind
speed input data from each turbine to known power curves to predict
how much power each turbine can produce at any given time. The PRM
sends individual power demands to each turbine with the objective
to obtain as close to power plant rated power as possible. The PRM
may be used with an extended power curve for an over-rated
turbine.
[0107] Embodiments of the invention enable the use of over-rating
at suitable times to lower the cost of energy. Within a wind power
plant, over-rating may be used selectively taking into account
variations in wind and site conditions across the wind park,
variations in rates of component wear and tear, turbine shut-downs
for maintenance or faults and variations in the price of
electricity. As turbine components fatigue at different rates in
different conditions, the actual lifetime of some components may be
considerably more than the 20 year expected lifetime for a wind
turbine. In a given set of conditions, the components that are
closest to their aggregate lifetime limit could have a low
instantaneous fatigue rate. As other components have a longer
lifetime as these are not driving the overall turbine life, the
turbine has spare production capacity. In addition, different
turbines in the power plant will experience different conditions
over their life.
[0108] Thus any turbine may be over-rated, if the conditions allow
it, to maximise energy output while maintaining the turbine
lifetime. This is illustrated by FIG. 10 a)-d). FIG. 10 a) shows
the total lifetime fatigue of various components. Component 5 is
the most critical, defining its 20 year life. FIG. 10 b) shows an
example of conditions where the instantaneous fatigue rate of
component 5 is lower that its 20 year average and that of component
7 is greater than its average. FIG. 10 c) shows that under these
conditions the turbine can be over-rated, bringing component 2,
which is now the one with the highest fatigue rate, up to its 20
year limit. FIG. 10 d) shows that the turbine can be over-rated
even more if component 2 is allowed to fatigue at a rate that is
higher than its lifetime limit. At this level of over-rating the
component would fail before the end of its 20 year lifetime but
this is not a problem for short periods of time as the component
has lifetime total fatigue to spare. This maximal over-rating is
therefore limited by accumulated fatigue rather than instantaneous
fatigue. In this case, component 7 does not have spare lifetime
capacity and so does not pass its 20 year limit. Thus, the turbines
can reduce the variability of the power plant output by acting as a
group
[0109] Lifetime usage estimators have been described in conjunction
with wind turbine components in the control of over-rating in wind
turbines. However, lifetime usage estimators may also be used in
other parts of a wind turbine power plant. The wind turbine
generator level comprises the plurality of wind turbine and
controllers described above. The power plant level comprises other
power plant components between the wind turbine generators and the
point of connection to the grid and includes the substation
transformer and cabling between the turbines and the substation
transformer and between the grid and the substation transformer.
Lifetime usage estimators can be used on these components to
provide inputs to a power plant optimiser in a similar manner to
the turbine optimiser described above.
[0110] Many alternatives to the embodiments described are possible
and will occur to those skilled in the art without departing from
the scope of the invention which is defined by the following
claims.
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